sid-clustering / README.md
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---
dataset_info:
features:
- name: sentences
dtype: string
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 19977137
num_examples: 8712
- name: test
num_bytes: 8607911
num_examples: 3735
download_size: 13060346
dataset_size: 28585048
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
## Dataset Summary
**SID Clustering (SIDClustring)** is a Persian (Farsi) dataset created for the **Clustering** task, specifically focusing on grouping academic articles. It is part of the [FaMTEB (Farsi Massive Text Embedding Benchmark)](https://huggingface.co/spaces/mteb/leaderboard). The dataset was constructed from scientific articles available on **SID (Scientific Information Database – sid.ir)**, categorized into 8 distinct domains reflecting academic disciplines.
* **Language(s):** Persian (Farsi)
* **Task(s):** Clustering (Document Clustering, Topic Modeling)
* **Source:** Crawled from the SID academic publication platform
* **Part of FaMTEB:** Yes
## Supported Tasks and Leaderboards
This dataset is designed to assess the ability of embedding models to perform document clustering—grouping articles into logical scientific categories. Results can be viewed on the [Persian MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard), under the Clustering task.
## Construction
1. Articles were collected by crawling the **sid.ir** platform.
2. For each article:
- The **title** and **abstract** were extracted.
- These were concatenated using two newline characters (`\n\n`) to form the document input.
3. Each document was assigned to one of 8 predefined SID categories.
4. The resulting dataset serves as a benchmark for evaluating unsupervised clustering performance.
## Data Splits
* **Train:** 8,712 samples
* **Development (Dev):** 0 samples
* **Test:** 3,735 samples